Surgical reporting for laparoscopic cholecystectomy based on phase annotation by a convolutional neural network (CNN) and the phenomenon of phase flickering: a proof of concept

Abstract

Purpose Surgical documentation is an important yet time-consuming necessity in clinical routine. Beside its core function to transmit information about a surgery to other medical professionals, the surgical report has gained even more significance in terms of information extraction for scientific, administrative and judicial application. A possible basis for computer aided reporting is phase detection by convolutional neural networks (CNN). In this article we propose a workflow to generate operative notes based on the output of the TeCNO CNN. Methods Video recordings of 15 cholecystectomies were used for inference. The annotation of TeCNO was compared to that of an expert surgeon (HE) and the algorithm based annotation of a scientist (HA). The CNN output then was used to identify aberrance from standard course as basis for the final report. Moreover, we assessed the phenomenon of ‘phase flickering’ as clusters of incorrectly labeled frames and evaluated its usability. Results The accordance of the HE and CNN was 79.7% and that of HA and CNN 87.0%. ‘Phase flickering’ indicated an aberrant course with AUCs of 0.91 and 0.89 in ROC analysis regarding number and extend of concerned frames. Finally, we created operative notes based on a standard text, deviation alerts, and manual completion by the surgeon. Conclusion Computer-aided documentation is a noteworthy use case for phase recognition in standardized surgery. The analysis of phase flickering in a CNN’s annotation has the potential of retrieving more information about the course of a particular procedure to complement an automated report.

The utility of synoptic operation reports in colorectal surgery: a systematic review

“…Familiarity with the SR components and optimisation of reports with auto-population features may further expedite data entry [ 2 ]. Bertlet et al demonstrated the potential to utilise machine learning and convolutional neural networks to populate SR from the operative video [ 19 ]. Additionally, cost savings related to reduced typing time (if reports were dictated) may be achieved with SR [ 10 , 12 ].…”